CN116112708B - Self-adaptive streaming media-oriented combined content storage, code rate conversion and power allocation resource optimization method - Google Patents
Self-adaptive streaming media-oriented combined content storage, code rate conversion and power allocation resource optimization method Download PDFInfo
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/239—Interfacing the upstream path of the transmission network, e.g. prioritizing client content requests
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
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- H04N21/61—Network physical structure; Signal processing
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- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
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- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
- H04N21/647—Control signaling between network components and server or clients; Network processes for video distribution between server and clients, e.g. controlling the quality of the video stream, by dropping packets, protecting content from unauthorised alteration within the network, monitoring of network load, bridging between two different networks, e.g. between IP and wireless
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- H04W72/00—Local resource management
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- H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
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Abstract
The invention relates to a method for optimizing combined content storage, code rate conversion and power distribution resources for self-adaptive streaming media, which comprises the following steps: firstly, collecting video content and video version requests of users by SBS; secondly, the SBS makes video buffering and transcoding decisions: the SBS establishes a joint content storage strategy and a code rate conversion strategy by combining storage and calculation resources of the MEC according to the collected request information of the user; thirdly, SBS information feedback and video transmission: for users capable of meeting the requirements, the SBS distributes proper transmission power for the users; for users who cannot meet the demand, the SBS feeds back the user request to the MBS, and the users acquire videos from the MBS. The invention can effectively reduce the interaction amount of the user and the video data of the core network and relieve network congestion; the method and the device can reduce the initial time delay of the video acquisition of the user, avoid video jamming and improve the video watching experience of the user.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a self-adaptive streaming media-oriented combined content storage, code rate conversion and power allocation resource optimization method.
Background
Video traffic occupies approximately 80% of the network traffic and is one of the main services in mobile communication networks. In the video transmission process, the problems of dynamic change of wireless transmission environment, different requirements of users for video definition and the like are faced. To overcome the dynamic environmental impact and meet the needs of various users, DASH (dynamic adaptive streaming media based on HTTP, DYNAMIC ADAPTIVE STREAMING over HTTP; HTTP: hypertext transfer protocol, hyperText Transfer Protocol) technology has been developed. DASH technology encodes video files into different bit rate versions, each of which is further divided into successive segments of duration 2-10 s. Thus, the user can download the corresponding bit rate version according to his own network conditions and preferences.
However, the application of DASH increases the volume of video traffic data due to the presence of multiple versions. The interaction of massive video data in the network is very easy to cause network congestion and even network paralysis, the time delay of the user for obtaining the video data is prolonged, and the user experience quality is reduced. In order to address the above challenges, MEC (mobile edge computing ) technology has received extensive attention from both academia and industry at home and abroad. By installing a server with storage and calculation capability in the SBS (small base station ) at the near-user side, MEC can store files with high popularity at the network edge on one hand, so as to reduce the video data interaction amount of the core network, thereby relieving network congestion; on the other hand, the MEC provides powerful computing resource support for conversion between video versions with different bit rates, namely, the MEC can realize conversion from high version to low version of the same video in a video transcoding mode, so that the MEC only needs to store the high-version video instead of all the versions of the video, storage efficiency is improved, and network congestion is further reduced.
In the existing MEC-assisted DASH network research, most of the storage capacity of MEC is only utilized, the auxiliary effect of MEC computing capacity on video storage is ignored, and the functions of MEC are difficult to fully utilize; in addition, because different versions of the video have different requirements on transmission rate, the BS is required to allocate corresponding transmission power for the user in the process of transmitting the video from the MEC to the user so as to meet the video downloading rate, and the video buffering time is prevented from being too long. Based on the above analysis, it can be known that, on the basis of fully utilizing the MEC storage and calculation functions, multi-domain resource scheduling such as content storage, code rate conversion and power allocation is needed to reduce video acquisition delay and improve user viewing experience.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a self-adaptive streaming media-oriented combined content storage, code rate conversion and power distribution resource optimization method. Through reasonable scheduling of multi-domain resources such as MEC side content storage, code rate conversion, power distribution and the like, the data interaction amount of a core network can be effectively reduced, network congestion is relieved, the initial time delay of a user for acquiring a video is further reduced, and the video watching experience of the user is improved.
The technical scheme of the invention is as follows:
a method for optimizing the joint content storage, code rate conversion and power distribution resources facing to self-adaptive streaming media comprises the following steps:
Step one, SBS collects user video content and video version request: the user request video content and version covered by the SBS, and the SBS collects video request information, position information and channel state information of all users; the video request information comprises video content and video version; the location information includes a distance between the user and the SBS; the channel state information includes fading factors of the user and the SBS communication link;
secondly, the SBS makes video content storage and code rate conversion decision: the SBS establishes a video cache, namely a joint content storage strategy and a code rate conversion strategy according to the request information of the user collected in the first step and combining with storage and calculation resources of the MEC;
Thirdly, SBS information feedback and video transmission: for users capable of meeting the requirements, the SBS distributes proper transmission power for the users according to the request information and the position information of the users, namely power distribution; for users who cannot meet the demand, the SBS feeds back the user request to the MBS, and the users acquire videos from the MBS.
According to the invention, the method and the system for establishing the adaptive streaming media network combined with content storage, code rate conversion and power allocation resource optimization in the MEC auxiliary adaptive streaming media network preferably have the following problems:
The objective function is as in formula (I):
Constraints include C1, C2, C3, C4;
wherein: the users are gathered into Video file set is/>Each video file is encoded as/>Version, version corresponding code rate isEach version is further divided into N video clips, and the duration of each video clip is phi; the storage capacity of MEC is C, the calculated capacity is D, and the maximum SBS transmitting power is P,/>Representing a video clip size, s k,l representing the video overall video file size, w representing the number of revolutions required to transcode a per bit video file, f representing the speed of the MEC processor; b, g m,σ2, I denote allocated bandwidth, channel gain, gaussian white noise and interference, respectively,/>Representing the transmit power allocated to a user by a base station,/>Representing the probability of a user requesting a file;
The optimization variables are: The cache variable x k,L epsilon {0,1}, the calculation variable/>, respectively Power distribution variable/>The matrix of the components, i c = {1,2, once again, i, L-1;
the optimization targets are as follows: minimizing the average time delay T (X, Y, P) of the video acquired by the user, wherein the specific expression is shown as a formula (II),
Wherein,For communication delay,/>For calculating the time delay, T 0 represents the time delay of the user downloading the file from the MBS;
The constraint conditions are as follows: c1 represents MEC storage capacity limitation, namely, the sum of MEC cache files does not exceed C; c2 represents the MEC calculation capacity limit, i.e. the total number of revolutions of the MEC transcoding file does not exceed D; c3 represents video download rate limitation, that is, the rate of downloading a file from SBS by a user requesting a video file (k, l) is not lower than R l to ensure smooth video playback; c4 denotes SBS transmit power limitation, i.e. the sum of the transmit powers allocated by SBS for the users does not exceed P.
In a preferred second step according to the present invention, the optimal video buffering, i.e. joint content storage strategy, and the transcoding strategy are obtained using MATLAB intlinprog functions.
According to the invention, in the third step, a heuristic algorithm of polynomial time complexity is adopted to quickly obtain a power allocation decision close to the optimal performance.
According to the invention, the method for optimizing the combined content storage, code rate conversion and power allocation resources preferably comprises the following steps:
1) Initializing: the MEC sets an initial content storage matrix X (i), a code rate conversion matrix Y (i) and a power allocation decision matrix P (i) as all-zero matrices, sets iteration step number i=1, and inputs system parameters into the MEC;
2) Variable calculation: fixing P (i), and solving by utilizing a MATLAB intlinprog function to obtain X (i+1) and Y (i+1); fixing X (i+1) and Y (i+1), solving to obtain P (i+1) by using a heuristic algorithm, and calculating T (i+1) according to an expression (II);
3) And (3) judging: determining whether T (i+1) is equal to T (i); if equal, the loop is terminated and variable matrices X (i+1),Y(i+1) and P (i+1) are output; if not, carrying out the step 4);
4) Variable updating: update P (i) to P (i+1) and repeat step 2).
A computer device comprising a memory and a processor, said memory storing a computer program, said processor implementing the steps of a method of joint content storage, rate conversion and power allocation resource optimization for adaptive streaming media when said computer program is executed.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method of joint content storage, rate conversion and power allocation resource optimization for adaptive streaming.
The beneficial effects of the invention are as follows:
The invention can effectively reduce the interaction amount of the user and the core network video data and relieve network congestion by reasonably scheduling the multi-domain resources such as content storage, code rate conversion, power distribution and the like from the perspective of a system; from the perspective of a user, the initial time delay of the user for acquiring the video can be reduced, video jamming is avoided, and the video watching experience of the user is improved.
Drawings
FIG. 1 is a flow chart of a method for optimizing combined content storage, rate conversion and power allocation resources according to the present invention;
FIG. 2 is a graph showing the average time delay of the prior art method and the method of the present invention compared with each other under different memory capacities of MEC;
FIG. 3 is a graph showing the average time delay of the prior art method and the method of the present invention compared with each other under different computing capacities of MEC;
Fig. 4 is a graph showing average delay comparison between the prior art method and the inventive method at different maximum transmission powers of SBS.
Detailed Description
The invention is further illustrated, but not limited, by the following examples.
Example 1
A self-adaptive streaming media-oriented combined content storage, code rate conversion and power allocation resource optimization method is used for relieving network congestion and reducing initial time delay of video acquisition of users, and comprises the following steps:
Step one, SBS collects user video content and video version request: the users covered by the SBS request video content and version according to personal preference and network state, and the SBS collects video request information, position information and channel state information of all users; the video request information comprises video content and video version; the location information includes a distance between the user and the SBS; the channel state information includes fading factors of the user and the SBS communication link;
Secondly, the SBS makes video content storage and code rate conversion decision: the SBS establishes a reasonable video cache, namely a joint content storage strategy and a code rate conversion strategy according to the request information of the user collected in the first step and combining with storage and calculation resources of the MEC;
Thirdly, SBS information feedback and video transmission: due to the limited storage and computing resources of MECs, part of the user needs cannot be met in SBS. In the step, for the users capable of meeting the requirements, the SBS distributes proper transmission power, namely power distribution, for the users according to the request information and the position information of the users; for users that cannot meet the demand, the SBS feeds back the user request to the MBS (macro base station ) from which the user will acquire the video.
Example 2
The method for optimizing the joint content storage, rate conversion and power allocation resources for the adaptive streaming media according to embodiment 1 is characterized in that:
The method establishes the problem of optimizing the combined content storage, code rate conversion and power distribution resources in the adaptive streaming media network which is suitable for MEC assistance as follows:
The objective function is as in formula (I):
Constraints include C1, C2, C3, C4;
wherein: assuming that there is one SBS in the network, the SBS is randomly distributed in the coverage area of the SBS All users can establish wireless communication connection with SBS or MBS, SBS is equipped with MEC server, its storage capacity is C, calculated capacity is D, and maximum emission power of SBS is P. Video file set is/>Each video file is encoded as/>Version, version corresponding coding rate is/>Each version is further divided into N video clips, and the duration of each video clip is phi; the probability of the mth user's request for a video file (k, l) is expressed as/>The storage capacity of MEC is C, the calculated capacity is D, and the maximum SBS transmitting power is P,/>Representing a video clip size, s k,l representing the video overall video file size, w representing the number of revolutions required to transcode a per bit video file, f representing the speed of the MEC processor; b, g m,σ2, I denote allocated bandwidth, channel gain, white gaussian noise and interference respectively,Representing the transmit power allocated to a user by a base station,/>Representing the probability of a user requesting a file;
The optimization variables are: The cache variable x k,L epsilon {0,1}, the calculation variable/>, respectively Power distribution variable/>A matrix of constituents, L c e {1,2, & gt, L-1};
the optimization targets are as follows: minimizing the average time delay T (X, Y, P) of the video acquired by the user, wherein the specific expression is shown as a formula (II),
Wherein,For communication delay,/>For calculating the time delay, T 0 represents the time delay of the user downloading the file from the MBS;
The constraint conditions are as follows: c1 represents MEC storage capacity limitation, namely, the sum of MEC cache files does not exceed C; c2 represents the MEC calculation capacity limit, i.e. the total number of revolutions of the MEC transcoding file does not exceed D; c3 represents video download rate limitation, that is, the rate of downloading a file from SBS by a user requesting a video file (k, l) is not lower than Rl to ensure smooth video playback; c4 denotes SBS transmit power limitation, i.e. the sum of the transmit powers allocated by SBS for the users does not exceed P.
Let x k,L e 0,1 be the stored decision variable of the MEC,The decision variables are calculated for the MEC. Due to the limited computation and storage capacity of MECs, x k,L and/>The following constraints need to be satisfied:
Where s k,L＝NRL phi represents the highest version video file size, w represents the number of revolutions required to transcode a per bit of video file, Representing a video clip size. The computational delay of MEC transcoding a video file (k, L) into (k, L c) is/>Where f represents the speed of the MEC processor.
Assume thatDecision variables for SBS power allocation, when the mth user is viewing video files (k, l), the user download rate can be expressed as/>Wherein B, g m,σ2, I represent allocated bandwidth, channel gain, white gaussian noise and interference, respectively. The communication latency of a user downloading a video file may be expressed as/>To ensure smooth video playback, the video download rate must exceed the video encoding rate, i.e
In addition, the total transmission power allocated by SBS to the user needs to be lower than its maximum transmission power:
Based on the MEC storage, calculation, and power allocation decisions, the average time delay for a user to acquire a video file can be expressed as:
thus, the latency minimization problem can be modeled as:
Objective function:
In the second step, the MATLAB intlinprog function is utilized to quickly and efficiently obtain the optimal video cache, namely the joint content storage strategy and the code rate conversion strategy.
The MATLAB intlinprog function obtains the optimal content storage strategy and the specific description of the code rate conversion strategy as follows:
(1) For video file k, when it stores decision x k,L and transcoding decision When determining, the storage space, code rate conversion resource, transmitting power and transmission delay occupied by the video file are determined (respectively using the symbols/>Representation). Thus, the storage decision x k,L and transcoding decision/>, of video file kCan be represented by the univariate z k,j, z k,j and x k,L,/>Corresponding relation of (C) and corresponding/>The values are shown in table 1.
TABLE 1
(2) The problem of solving the variable z k,j can be translated into
The target equation:
constraints include C5, C6, C7.
The problem (3) is an integer linear programming equation, the z k,j can be obtained by quickly and efficiently solving the MATLAB intlinprog functions, and the specific function calling mode is as follows: z= intlinprog (f, intcon, a, b, aeq, beq, lb, ub); wherein the method comprises the steps ofAn array of z k,j,/>For the time delay/>Constituent array,/>Lines 1,2, 3 are each represented by/>A matrix of components, b= [ C; d, a step of performing the process; p ], aeq =zeros (K, KJ), beq =ons (K, 1), lb=zeros (KJ, 1), ub=ons (KJ, 1), intcon =1:kj.
After Z is obtained, the content storage matrix X and the transcoding strategy matrix Y can be restored according to the table 1.
In the third step, a heuristic algorithm with polynomial time complexity is adopted to quickly obtain a power allocation decision close to the optimal performance.
The polynomial time complexity heuristic algorithm is specifically described as follows:
(1) Using the formula Obtaining SBS minimum transmitting power/>, which is required by mth user to smoothly play video file (k, l)
(2) From X and Y, calculate the total power
(3) The SBS residual transmitting power P 1＝P-P0 can be obtained according to the P 0;
(4) P 1 is evenly distributed to all users requesting low-version video files.
As shown in fig. 1, the method for optimizing the combined content storage, rate conversion and power allocation resources comprises the following steps:
1) Initializing: the MEC sets the initial content storage matrix X (i), the code rate conversion matrix Y (i), the power allocation decision matrix P (i) to an all-zero matrix, sets the iteration step number i=1, and sets the system parameters (including M, C, D, N，φf，w，B，gm,σ2，I，/>D m, P) input MEC;
2) Variable calculation: fixing P (i), and solving by utilizing the MATLAB intlinprog function in the second step to obtain X (i+1) and Y (i+1); fixing X (i+1) and Y (i+1), solving to obtain P (i+1) by utilizing the third heuristic algorithm, and calculating T (i+1) according to an expression (II);
3) And (3) judging: determining whether T (i+1) is equal to T (i); if equal, the loop is terminated and variable matrices X (i+1),Y(i+1) and P (i+1) are output; if not, carrying out the step 4);
4) Variable updating: update P (i) to P (i+1) and repeat step 2).
The prediction performance of the prediction method provided by the invention is tested and evaluated, and compared with the traditional algorithm and the real flow value, the specific result is as follows:
Fig. 2 shows a schematic diagram of the mean time delay of the proposed method and three prior art methods as a function of MEC storage capacity. It can be seen that as the MEC storage capacity increases, the average latency of all methods decreases. Because the MEC can store more video files, more user requests can be satisfied from the MEC without downloading from the MBS, which is much more time-delayed than the time-delayed downloading from the MEC, and thus the time-delay is progressively lower. It can also be seen from fig. 2 that the proposed method performs far beyond the other three methods. Specifically, the average time delay of the method is 10.87-14.60% lower than that of method 1, 20.64-31.02% lower than that of method 2, and 30.14-42.25% lower than that of method 3.
Fig. 3 shows a schematic diagram of the mean time delay of the proposed method and three prior art methods as a function of the MEC calculation capacity. It can be seen that as the MEC computation capacity increases, the average latency of the proposed method decreases. Because the MEC can transcode more video files, more lower versions of user requests can be satisfied from the MEC. In addition, along with the change of calculation capacity, the method still has excellent performance, and the average time delay is 5.96% -16.57% lower than that of the method 1, 25.72% -32.44% lower than that of the method 2, and 14.52% -49.48% lower than that of the method 3.
Fig. 4 shows the average time delay of the proposed method and three prior art methods as a function of SBS maximum transmit power. When the maximum transmission power of the SBS is increased, the average time delay of the method is gradually reduced. Because SBS can allocate larger transmission power to users, the download rate of users gradually increases, and thus the download delay decreases. The performance of the method is still superior to other methods along with the change of the maximum transmission power of the SBS. Specifically, the average time delay is 4.64-12.47% lower than that of method 1, 11.53-31.02% lower than that of method 2, and 32.46-42.25% lower than that of method 3.
Example 3
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the adaptive streaming oriented joint content storage, rate conversion and power allocation resource optimization method described in embodiments 1 or 2 when executing the computer program.
Example 4
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the adaptive streaming oriented joint content storage, rate conversion and power allocation resource optimization method described in embodiments 1 or 2.
Claims (5)
1. The method for optimizing the combined content storage, code rate conversion and power distribution resources for the self-adaptive streaming media is characterized by comprising the following steps of:
Step one, SBS collects user video content and video version request: the user request video content and version covered by the SBS, and the SBS collects video request information, position information and channel state information of all users; the video request information comprises video content and video version; the location information includes a distance between the user and the SBS; the channel state information includes fading factors of the user and the SBS communication link;
secondly, the SBS makes video content storage and code rate conversion decision: the SBS establishes a video cache, namely a joint content storage strategy and a code rate conversion strategy according to the request information of the user collected in the first step and combining with storage and calculation resources of the MEC;
thirdly, SBS information feedback and video transmission: for users capable of meeting the requirements, the SBS distributes proper transmission power for the users according to the request information and the position information of the users, namely power distribution; for users incapable of meeting the requirements, the SBS feeds back the user request to the MBS, and the users acquire videos from the MBS;
in the second step, an optimal video cache, namely a joint content storage strategy and a code rate conversion strategy are obtained by utilizing MATLAB intlinprog functions; comprising the following steps:
(1) For video file k, when it stores decision x k,L and transcoding decision When determining, the storage space, code rate conversion resource, transmitting power and transmission delay occupied by the video file are determined and respectively expressed as/>
(2) Problem transformation to solve the variable z k,j
The target equation:
constraints include C5, C6, C7;
Is an integer linear programming equation, z k,j is obtained by quick and efficient solving by MATLAB intlinprog functions, and the specific function calling mode is as follows: z= intlinprog (f, intcon, a, b, aeq, beq, lb, ub); wherein, An array of z k,j,/>For the time delay/>Constituent array,/>Lines 1,2, 3 are each represented by/>A matrix of components, b= [ C; d, a step of performing the process; p ], aeq =zeros (K, KJ), beq =ons (K, 1), lb=zeros (KJ, 1), ub=ons (KJ, 1), intcon =1:kj; recovering a content storage matrix X and a transcoding strategy matrix Y after Z is obtained;
In the third step, a heuristic algorithm of polynomial time complexity is adopted to quickly obtain a power allocation decision close to the optimal performance, which comprises the following steps:
a. using the formula Obtaining SBS minimum transmitting power/>, which is required by mth user to smoothly play video file (k, l)
B. from X and Y, calculate the total power
C. Obtaining SBS residual transmission power P 1＝P-P0 according to P 0;
d. P 1 is evenly distributed to all users requesting low-version video files.
2. The method for optimizing the joint content storage, rate conversion and power allocation resources for the adaptive streaming media according to claim 1, wherein the method is characterized in that the joint content storage, rate conversion and power allocation resource optimization problem applicable to the MEC-assisted adaptive streaming media network is established as follows:
The objective function is as in formula (I):
Constraints include C1, C2, C3, C4;
wherein: the users are gathered into Video file set is/>Each video file is encoded as/>Version, version corresponding code rate isEach version is further divided into N video clips, and the duration of each video clip is phi; the storage capacity of MEC is C, the calculated capacity is D, and the maximum SBS transmitting power is P,/>Representing a video clip size, s k,l representing the video overall video file size, w representing the number of revolutions required to transcode a per bit video file, f representing the speed of the MEC processor; b, g m,σ2, I denote allocated bandwidth, channel gain, gaussian white noise and interference, respectively,/>Representing the transmit power allocated to a user by a base station,/>Representing the probability of a user requesting a file;
The optimization variables are: The cache variable x k,L epsilon {0,1}, the calculation variable/>, respectively Power distribution variable/>A matrix of constituents, L c e {1,2, & gt, L-1};
the optimization targets are as follows: minimizing the average time delay T (X, Y, P) of the video acquired by the user, wherein the specific expression is shown as a formula (II),
Wherein,For communication delay,/>For calculating the time delay, T 0 represents the time delay of the user downloading the file from the MBS;
The constraint conditions are as follows: c1 represents MEC storage capacity limitation, namely, the sum of MEC cache files does not exceed C; c2 represents the MEC calculation capacity limit, i.e. the total number of revolutions of the MEC transcoding file does not exceed D; c3 represents video download rate limitation, that is, the rate of downloading a file from SBS by a user requesting a video file (k, l) is not lower than R l to ensure smooth video playback; c4 denotes SBS transmit power limitation, i.e. the sum of the transmit powers allocated by SBS for the users does not exceed P.
3. The method for optimizing the joint content storage, rate conversion and power allocation resources for adaptive streaming media according to any one of claims 1 or 2, wherein the method for optimizing the joint content storage, rate conversion and power allocation resources comprises the following steps:
1) Initializing: the MEC sets an initial content storage matrix X (i), a code rate conversion matrix Y (i) and a power allocation decision matrix P (i) as all-zero matrices, sets iteration step number i=1, and inputs system parameters into the MEC;
2) Variable calculation: fixing P (i), and solving by utilizing a MATLAB intlinprog function to obtain X (i+1) and Y (i+1); fixing X (i+1) and Y (i+1), solving to obtain P (i+1) by using a heuristic algorithm, and calculating T (i+1) according to an expression (II);
3) And (3) judging: determining whether T (i+1) is equal to T (i); if equal, the loop is terminated and variable matrices X (i+1),Y(i+1) and P (i +1) are output; if not, carrying out the step 4);
4) Variable updating: update P (i) to P (i+1) and repeat step 2).
4. A computer device comprising a memory and a processor, said memory storing a computer program, characterized in that said processor, when executing said computer program, implements the steps of the method for adaptive streaming oriented joint content storage, rate conversion and power allocation resource optimization of any of claims 1-3.
5. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor performs the steps of the method for joint content storage, rate conversion and power allocation resource optimization for adaptive streaming according to any of claims 1-3.
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